In recent years, as the development of the image recognition technology, the segmentation technology for segmenting the organs from the medical images has attracted more and more attention. For example, lung segmentation plays an important role in visualization and quantitative analysis of lung parenchyma. Moreover, 3D lung segmentation technology and lung vessel excluding technology are of great significance for accelerating the diagnosis procedure.
In the prior art, generally the images acquired by CT (computerized Tomography) are employed for lung segmentation, since MRI (Magnetic Resonance Imaging) apparatus is unsuitable for depicting the lung, but more suitable for the head. At present, the are many techniques for CR images based lung segmentation and lung vessel extraction.
For example, patent document 1 (U.S. Pat. No. 9,042,620) discloses a method for multi-organ segmentation and a lung segmentation for three-dimensional CT images, wherein marginal space learning method is used to generate initialized mesh for level set, and level set is used to get a distance map based accurate segmentation.
Patent document 2 (CN102243759B) discloses a geometric deformation model based three-dimensional lung vessel image segmentation method, wherein level set function is used to segment lung vessel for high-resolution and high-contrast CT images.
However, the segmentation methods of patent document 1 and patent document 2 are not suitable for the images acquired by MRI apparatus. For MRI apparatus, it is difficult to perform accurate 3D lung segmentation and lung vessel excluding with existing technology.
For example, the non-patent document 1 (“Distance Regularized Level Set Evolution and Its Application to image Segmentation [J], Chunming Li, Chenyuang Xu etc., IEEE Transactions on Image Processing”) discloses a method applicable to lung segmentation.
In lung segmentation of medical images, a commonly used method is: firstly inputting the image data acquired by a medical image acquisition apparatus; performing preliminary lung segmentation using the rough segmentation methods such as threshold method or binarization segmentation method on the basis of the image data to extract the lung image; and then refining the preliminary segmentation results to obtain more accurate segmentation results.
However, the existing segmentation technique is not sensitive to the low contrast areas on boundaries between the lung parenchyma and the chest wall, and is ineffective in the processing of cavity structure (such as vessels and nodule/tumor).
FIG. 13 is an exemplary figure representing the results of the existing lung segmentation processing for the chest image acquired by MRI apparatus. As shown in FIG. 13, the data of chest wall tissue is still left in the area of the extracted lung image pointed by arrow A after refining. On the other hand, the thin vessels could be included, but the relatively thick vessels cannot be correctly included, as shown in the area pointed by arrow B, meanwhile, the nodules area as pointed by arrow C cannot be included as well.